Staff Data Engineer

Shyftlabs; Toronto; Hybrid; CAD 90,000 - 130,000; Full time

Job summary

A leading company in data solutions is seeking a Staff Data Engineer to build and optimize enterprise data pipelines for Fortune 500 clients. This role involves implementing robust data processing infrastructure with a focus on both batch and streaming data workloads, alongside collaboration with client engineering teams. A strong background in Databricks and AWS data services is essential for success in this position.

Benefits

Competitive salary

Strong healthcare insurance

Extensive learning and development resources

Flexible working arrangements

Qualifications

• 6+ years of experience in enterprise data engineering with Fortune 500 companies.
• Expert-level experience with Databricks platform.
• Strong background in Delta Live Tables (DLT) and streaming data processing.

Responsibilities

• Implement and optimize enterprise-scale data pipeline architecture for multiple business domains.
• Troubleshoot and resolve pipeline reliability issues.
• Lead technical workshops and provide hands-on guidance.

Skills

Pipeline optimization

Data analysis

Technical consulting

Performance optimization

Education

Bachelor's or Master's degree in Computer Science

Tools

Databricks

Active Batch

AWS

Apache Spark

Job description

Position Overview:

ShyftLabs is seeking an experienced Staff Data Engineer to implement and optimize enterprise data pipelines and ingestion frameworks for Fortune 500 companies. You will build robust Databricks-based data processing infrastructure, implement pipeline reliability solutions, and create scalable technical solutions for both batch and streaming data workloads.

ShyftLabs is a growing data product company founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation.

Job Responsibilities:

• Implement and optimize enterprise-scale data pipeline architecture processing 1500+ daily jobs across multiple business domains
• Build and maintain Databricks job orchestration solutions integrated with Active Batch scheduling platforms
• Implement data ingestion frameworks supporting CDC via AWS DMS and S3-based data lake patterns
• Troubleshoot and resolve pipeline reliability issues including overrun problems, dependency failures, and compute optimization
• Build observability and monitoring solutions for large-scale data operations teams
• Implement near real-time ingestion patterns using DLT (Delta Live Tables) and streaming architectures
• Build and maintain data quality frameworks integrated with data governance processes
• Implement serverless migration strategies and optimize cloud resource utilization
• Lead technical workshops and provide hands-on guidance to client engineering teams

• Enterprise Pipeline Expertise: Proven experience building and optimizing data pipelines at Fortune 500 scale
• Operational Excellence: Deep understanding of pipeline monitoring, alerting, and failure recovery patterns
• Performance Optimization: Ability to analyze and optimize compute resources and job execution patterns
• Technical Consulting: Strong workshop facilitation and client relationship skills, with ability to present technical solutions and lead hands-on training sessions with client engineering teams

Basic Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or related technical field
• 6+ years of experience in enterprise data engineering with Fortune 500 companies
• Expert-level experience with Databricks platform including job scheduling and cluster management
• Hands-on experience with Active Batch or similar enterprise job orchestration platforms
• Deep knowledge of AWS data services including DMS, S3, and database connectivity
• Proven experience with CDC (Change Data Capture) implementations at enterprise scale
• Strong background in Delta Live Tables (DLT) and streaming data processing
• Understanding of Apache Spark optimization and performance tuning
• Experience with enterprise database integration and cross-platform data connectivity

Preferred Qualifications

• Experience with enterprise integration patterns and API data ingestion
• Knowledge of cloud storage optimization and data lake best practices
• Background in high-volume transaction processing systems
• Experience with retail or energy industry data patterns
• Familiarity with trading and pricing systems data workflows

We are proud to offer a competitive salary alongside a strong healthcare insurance and benefits package. The role is preferably hybrid, with 2 days per week spent in the office, and flexibility for client engagement needs. We pride ourselves on the growth of our employees, offering extensive learning and development resources.

ShyftLabs is an equal-opportunity employer committed to creating a safe, diverse and inclusive environment. We encourage qualified applicants of all backgrounds including ethnicity, religion, disability status, gender identity, sexual orientation, family status, age, nationality, and education levels to apply. If you are contacted for an interview and require accommodation during the interviewing process, please let us know.